CN104925063A - Model predictive control method for electromechanical composite transmission vehicle - Google Patents

Model predictive control method for electromechanical composite transmission vehicle Download PDF

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Publication number
CN104925063A
CN104925063A CN201510189205.3A CN201510189205A CN104925063A CN 104925063 A CN104925063 A CN 104925063A CN 201510189205 A CN201510189205 A CN 201510189205A CN 104925063 A CN104925063 A CN 104925063A
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control
predictive control
electromechanical combined
vehicle
time domain
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王伟达
韩立金
项昌乐
刘辉
马越
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Beijing Institute of Technology BIT
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Beijing Institute of Technology BIT
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    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60WCONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W10/00Conjoint control of vehicle sub-units of different type or different function
    • B60W10/04Conjoint control of vehicle sub-units of different type or different function including control of propulsion units
    • B60W10/06Conjoint control of vehicle sub-units of different type or different function including control of propulsion units including control of combustion engines
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60WCONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W10/00Conjoint control of vehicle sub-units of different type or different function
    • B60W10/04Conjoint control of vehicle sub-units of different type or different function including control of propulsion units
    • B60W10/08Conjoint control of vehicle sub-units of different type or different function including control of propulsion units including control of electric propulsion units, e.g. motors or generators
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60WCONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W10/00Conjoint control of vehicle sub-units of different type or different function
    • B60W10/18Conjoint control of vehicle sub-units of different type or different function including control of braking systems
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60WCONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2510/00Input parameters relating to a particular sub-units
    • B60W2510/06Combustion engines, Gas turbines
    • B60W2510/0638Engine speed
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60WCONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2510/00Input parameters relating to a particular sub-units
    • B60W2510/24Energy storage means
    • B60W2510/242Energy storage means for electrical energy
    • B60W2510/244Charge state
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60WCONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2710/00Output or target parameters relating to a particular sub-units
    • B60W2710/06Combustion engines, Gas turbines
    • B60W2710/0666Engine torque
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60WCONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2710/00Output or target parameters relating to a particular sub-units
    • B60W2710/08Electric propulsion units
    • B60W2710/083Torque
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60WCONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/105Speed
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02TCLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00Road transport of goods or passengers
    • Y02T10/60Other road transportation technologies with climate change mitigation effect
    • Y02T10/62Hybrid vehicles

Abstract

The invention relates to a model predictive control method for an electromechanical composite transmission vehicle. The method includes the steps that a system model, a future required torque algorithm and the like are used as prediction models according to the location of a driver pedal at the current sampling moment, in combination with the vehicle state signals such as the speed, the rotation speed of an engine and the state of charge (SOC) of a battery, and future vehicle required torque information from a self-adaption recursive prediction algorithm, a control command for components of the system within the control cycle is determined through model predictive control, and on-line optimal control is realized. Due to the fact that model predictive control takes the future vehicle requirement information and the optimized objective function performance within the predicted time domain into consideration comprehensively, it can be guaranteed that the optimal control quantity applied to the system currently has no adverse effects on future behaviors of the system, and a better control effect can be obtained.

Description

The model predictive control method of electromechanical combined driven vehicle
Technical field
The present invention relates to a kind of model predictive control method of electromechanical combined driven vehicle, electromechanical combined drive technology field.
Background technology
Energy-saving and environmental protection and safety are three large themes of current development of automobile.At present, Global Auto recoverable amount is more than 500,000,000, and automobile has become one of people's main traffic mode, but brings enormous pressure also to environment and the energy.The power system combustion engine of orthodox car is that the mode of burning produces power, solve energy conservation and environmental protection, and must solve with energy resource consumption is the power system net effciency problem of cost.Therefore, how to improve efficiency of energy utilization, reduce pollution to environment, become the primary problem of automotive system design and Systematical control.Electronlmobil comprises pure electric automobile (EV), hybrid vehicle (HEV) and fuel cell powered vehicle (FCV) three kinds of forms, but pure electric vehicle is owing to being subject to the obstruction of technical matters, and development is restricted.Fuel cell powered vehicle has the feature of energy-efficient and anti-emission carburetor, but the generation of hydrogen, storage and configuration are the current ultimate challenges faced, and fuel-cell vehicle comes into the market still to need to experience quite long process.For this reason, hybrid vehicle becomes the vehicle most at present with industrialization and market-oriented prospect.
Electromechanical combined transmission belongs to a kind of pattern of series parallel hybrid power vehicle, it by multiple motor and planet arrangement mechanism by engine speed torque and vehicle speed and demand torque decoupler, make under the prerequisite of satisfied traveling demand, engine working point can be regulated more flexibly, optimize vehicle performance.And energy management control policy coordinates each propulsion source power division exactly, it is the core technology of electromechanical combined driving system.At present, business circles and academia conduct extensive research these control policies, the rule-based control policy of most employing, though it easily realizes, but control policy is simple, can not realize the optimal control of power division.Also have simultaneously and adopt based on optimized control policy, as dynamic programming controls and the control of equivalent fuel oil consumption, but dynamic programming controls cannot be used for because of its non-causality and excessive calculated amount controlling in real time and depends critically upon selection and the identification of driving cycles, equivalent fuel oil consumption controls to improve fuel economy well because it is short-sighted again.
Summary of the invention
The object of the present invention is to provide a kind of model predictive control method of electromechanical combined driven vehicle, Simplified analysis is carried out to electromechanical combined train physical model, obtains the math modeling of whole system; By the torque of self adaptation recursive prediction algorithm predicts Shape Of Things To Come demand.In order to effectively process the contradiction of engine optimum fuel economy and battery SOC minimal ripple scope, using system mathematic model and Shape Of Things To Come demand torque algorithm etc. as forecast model, electromechanical combined driven vehicle energy management predictive controller is devised according to Model Predictive Control Theory, to improve fuel economy for main objective, by output and the controlling quantity of system in forecast model prediction following a period of time, adopt the mode of " walking while optimize " to realize real-time optimal control.
To achieve these goals, technical scheme of the present invention is as follows.
A kind of model predictive control method of electromechanical combined driven vehicle, according to the driver pedal position of current sample time, in conjunction with the Vehicular status signals such as the speed of a motor vehicle, engine speed and battery charge state SOC and the Shape Of Things To Come demand moment information coming from self adaptation recursive prediction algorithm, using system model and tomorrow requirement torque algorithm etc. as forecast model, set up electromechanical combined driven vehicle model predictive control method, determined the various parts control command in this control cycle by Model Predictive Control.Because Model Predictive Control has considered the optimization object function performance in prediction time domain, can ensure that the optimal control codes of the system that is currently applied to can not produce adverse influence to the behavior of system in future, better control effects can be obtained.
The control flow of electromechanical combined actuation system models predictive control, concrete steps are as follows:
(1) sampling obtains current system status information, applies the torque-demand information in recursive prediction algorithm acquisition system prediction time domain simultaneously, and calculates the speed information in prediction time domain by this moment information;
(2) at current sampling point place, application Taylor series formal expansion, is converted into linear model by non-linear electromechanical combined actuation system models;
(3) in prediction time domain, convert system model to discrete model, discrete steps is T s=T p/ N p;
(4) apply linear MPC method, calculate the Model Predictive Control problem solving electromechanical combined driving system;
(5) optimization solution will obtained, is applied to electromechanical combined driving system and controls;
(6) return step (1) in the next systematic sampling moment, repeat above step.
In simulation process, the main adjustment parameter that Model Predictive Control is used comprises: weight coefficient every in objective function, prediction time domain p and control time domain m etc.By analyzing simulation result, according to real-time system torque demands, the weight coefficient in optimization object function being adjusted, the performance of control policy can be improved by actv..
Because engine operation is higher in high torque (HT) region aging rate, when the demand torque ratio of system is larger, suitably should reduce the weight coefficient of engine consumption, increase the weight coefficient of battery SOC simultaneously, make driving engine more tend to be operated in the higher region of power, thus improve the efficiency of electromechanical combined driving system; When system requirements torque is less, the weight coefficient of suitable increase engine consumption, utilizes battery to carry out the power demand of balanced system; When demand torque is for time negative, system is in braking mode, should choose less SOC weight coefficient, makes the energy braked can obtain actv. and reclaims.
This beneficial effect of the invention is: the present invention is by the electromechanical combined driven vehicle energy management strategies based on Model Predictive Control, to improve fuel economy for main objective, using system model and tomorrow requirement torque algorithm etc. as forecast model, by output and the controlling quantity of system in forecast model prediction following a period of time, adopt the mode of " walking while optimize " to realize real-time optimal control.Establish electromechanical combined driving system vehicle dynamic model, to improve car load fuel economy for target, devise the model predictive controller having and control in real time potential quality, disclose Model Predictive Control Theory and method for the feasibility of series parallel type electromechanical combined driven vehicle energy management control policy and validity by the modeling and simulation of electromechanical combined driven vehicle Model Predictive Control.Simulation result shows this Model Predictive Control strategy to a certain extent can the fuel economy of gig electricity compound transmission vehicle.For the design of further real-time power management and controlling tactics and test provide theoretical basis.
Accompanying drawing explanation
Fig. 1 be in the embodiment of the present invention use electromechanical combined drive model predictive control constructional drawing.
Fig. 2 be in the embodiment of the present invention use the control flow chart of electromechanical combined drive model predictive control.
Detailed description of the invention
Below in conjunction with drawings and Examples, the specific embodiment of the present invention is described, better to understand the present invention.
Embodiment
The structure of the electromechanical combined actuation system models predictive control in the present embodiment, as shown in Figure 1.The task of electromechanical combined actuation system models predictive control strategy is, according to the driver pedal position (system torque demands) of current sample time, in conjunction with the Vehicular status signals such as the speed of a motor vehicle, engine speed and battery charge state SOC and the Shape Of Things To Come demand moment information coming from self adaptation recursive prediction algorithm, by calculating the various parts control command determined in this control cycle.Because Model Predictive Control has considered the optimization object function performance in prediction time domain, can ensure that the optimal control codes of the system that is currently applied to can not produce adverse influence to the behavior of system in future, better control effects can be obtained.
The control flow of electromechanical combined actuation system models predictive control, as shown in Figure 2.Its concrete steps are as follows:
(1) sampling obtains current system status information, applies the torque-demand information in recursive prediction algorithm acquisition system prediction time domain simultaneously, and calculates the speed information in prediction time domain by this moment information.
System requirements torque prediction model, is obtained by recursive prediction model
Wherein, Θ ~ 2 n ( k ) = [ a 0 ( k ) , a 1 ( k ) , ... , a n - 1 ( k ) , b 0 ( k ) , b 1 ( k ) , ... , b n - 1 ( k ) ] T For the ARX model regression vector during sampling of kth step, it is defined as:
The task of electromechanical combined driving system energy management control policy is, according to the driver pedal position (system torque demands) of current time, in conjunction with Vehicular status signals such as the speed of a motor vehicle, engine speed and battery charge state SOC, by calculating the various parts control command determined in this control cycle.Due to the dynamic characteristics of system torque demands, therefore this control problem is a dynamic decision problem.According to the property requirements of electromechanical combined driving system, under the condition meeting system restriction, the optimization aim of energy management control policy makes system obtain best fuel economy, keeps the SOC of battery within rational scope simultaneously.Therefore, the objective function defining electromechanical combined driving system optimization problem is:
min J = ∫ t t + Δ t ( W L e ( L e ( t ) ) 2 + W S O C ( S O C ( t ) - SOC r ) 2 + W w e ( ω e n g ( t ) - ω e n g r ( t ) ) 2 ) d t
In formula, W le, W sOC, W webe respectively the weight coefficient of engine consumption, battery SOC and engine speed;
SOC r, ω engrbe respectively the expected value of battery SOC and engine speed.
Meanwhile, electromechanical combined driving system optimization problem, go back the following constraint condition of demand fulfillment:
ω A_min≤ω A≤ω A_max,T A_minA)≤T A≤T A_maxA)
ω B_min≤ω B≤ω B_max,T B_minB)≤T B≤T B_maxB)
SOC min≤SOC≤SOC max,P bat_min(SOC)≤P bat(SOC)≤P bat_max(SOC)
(2) at current sampling point place, application Taylor series formal expansion, is converted into linear model by non-linear electromechanical combined actuation system models.
Model Predictive Control of the present invention is mainly based on the analysis that linear system is carried out, and in order to apply it to electromechanical combined driving system, needs will carry out linearization to system model.Adopt Taylor series expansion by non-linear electromechanical combined actuation system models linearization, and hypothesis original system in whole prediction time domain can represent with the model after current linear.At current time, the system model after linearization is:
x · = A ~ x + B ~ u u + B ~ v v n e w y = C ~ x
(3) in prediction time domain, convert system model to discrete model, discrete steps is T s=T p/ N p.
Be more than the continuous system model of electromechanical combined transmission, in order to meet the demand in line computation, need in actual applications to convert thereof into discrete model in prediction time domain, discrete steps is T s=T p/ N p.Meanwhile, conveniently process and input relevant constraint, getting the incremental form Δ U of system control amount, Δ U (k)=U (k) req-U (k).
Consider that the state space incremental model of linear discrete time system is as follows:
Δ x ( k + 1 ) = A ~ Δ x ( k ) + B ~ u Δ u ( k ) + B ~ v Δ v ( k ) , y ( k ) = C ~ Δ x ( k ) + y ( k - 1 )
Wherein,
Δx(k)=x(k)-x(k-1),
Δu(k)=u(k)-u(k-1),
Δv(k)=v(k)-d(v-1),
In model it is state increment; it is control inputs increment; it is the external disturbance increment that can measure; it is system output; the system matrix of corresponding dimension respectively.
(4) apply linear MPC method, calculate the Model Predictive Control problem solving electromechanical combined driving system.
The task of electromechanical combined actuation system models predictive control strategy is, according to the driver pedal position (system torque demands) of current sample time, in conjunction with the Vehicular status signals such as the speed of a motor vehicle, engine speed and battery charge state SOC and the Shape Of Things To Come demand moment information coming from self adaptation recursive prediction algorithm, by calculating the various parts control command determined in this control cycle.Because Model Predictive Control has considered the optimization object function performance in prediction time domain, can ensure that the optimal control codes of the system that is currently applied to can not produce adverse influence to the behavior of system in future, better control effects can be obtained.
(5) optimization solution will obtained, is applied to electromechanical combined driving system and controls;
(6) return step (1) in the next systematic sampling moment, repeat above step.
In simulation process, the main adjustment parameter that Model Predictive Control is used comprises: weight coefficient every in objective function, prediction time domain p and control time domain m etc.By analyzing simulation result, according to real-time system torque demands, the weight coefficient in optimization object function being adjusted, the performance of control policy can be improved by actv..
Electromechanical combined actuation system models predictive control strategy can be good at the tracking of target vehicle speed, realizes the power demand of system, ensures the dynamic property of vehicle.For the state-of-charge SOC of battery, under seeing two kinds of control policies on the whole, can both keep SOC within certain zone of reasonableness, but present again different variation tendencies from both battery SOCs of local.In predictive control strategy, the weight coefficient in system optimization target can change along with the difference of system demand power.When the lower demand power of car speed is less, the value that in optimization aim, the weight coefficient meeting selection and comparison of battery SOC is little, now system tends to battery discharge, and therefore the SOC of battery can reduce accordingly; And along with the increase of the speed of a motor vehicle, when system demand power is larger, in order to make engine operation in the higher region of efficiency, the weight coefficient of fuel oil consumption can corresponding reduction, now the power of driving engine is larger, except for except driving vehicle, unnecessary power can be got up by battery storage, causes the rising of battery SOC.From simulation result, in system the variation tendency of battery SOC and the setting of control policy consistent.
Below the system fuel economy under predictive control strategy is analyzed, electromechanical combined driving system equivalence hundred kilometers of fuel oil consumptions that can be obtained under predictive control by emulation are 25.83L, compared with the 28.96L of rule-based control policy, improve 10.8%, compared to the 25.96L of Automatic adjusument Optimal Control Strategy, also slightly improve.Illustrate that predictive control strategy all has advantage compared to rule and policy and self_adaptive adjusting strategy in the control of online calculating and driving engine dynamic property in real time, system fuel economy has greatly improved compared to the control policy of rule.
The above is the preferred embodiment of the present invention; it should be pointed out that for those skilled in the art, under the premise without departing from the principles of the invention; can also make some improvements and modifications, these improvements and modifications are also considered as protection scope of the present invention.

Claims (2)

1. the model predictive control method of an electromechanical combined driven vehicle, it is characterized in that: according to the driver pedal position of current sample time, in conjunction with the Vehicular status signals such as the speed of a motor vehicle, engine speed and battery charge state SOC and the Shape Of Things To Come demand moment information coming from self adaptation recursive prediction algorithm, using system model and tomorrow requirement torque algorithm etc. as forecast model, set up electromechanical combined driven vehicle model predictive control method, determined the various parts control command in this control cycle by Model Predictive Control.
2. the model predictive control method of electromechanical combined driven vehicle according to claim 1, is characterized in that: the control flow of described electromechanical combined actuation system models predictive control, and concrete steps are as follows:
(1) sampling obtains current system status information, applies the torque-demand information in recursive prediction algorithm acquisition system prediction time domain simultaneously, and calculates the speed information in prediction time domain by this moment information;
(2) at current sampling point place, application Taylor series formal expansion, is converted into linear model by non-linear electromechanical combined actuation system models;
(3) in prediction time domain, convert system model to discrete model, discrete steps is T s=T p/ N p;
(4) apply linear MPC method, calculate the Model Predictive Control problem solving electromechanical combined driving system;
(5) optimization solution will obtained, is applied to electromechanical combined driving system and controls;
(6) return step (1) in the next systematic sampling moment, repeat above step.
CN201510189205.3A 2015-04-12 2015-04-12 Model predictive control method for electromechanical composite transmission vehicle Pending CN104925063A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107688343A (en) * 2017-08-01 2018-02-13 北京理工大学 A kind of energy control method of motor vehicle driven by mixed power
CN109572665A (en) * 2017-09-29 2019-04-05 通用汽车环球科技运作有限责任公司 Inearized model based on dynamical system MPC
CN111409622A (en) * 2020-01-17 2020-07-14 北京理工大学 Control method and device for electromechanical compound transmission system of tracked vehicle

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103625462A (en) * 2013-08-01 2014-03-12 河南科技大学 Method for controlling energy-saving series-connection hybrid power tractor

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103625462A (en) * 2013-08-01 2014-03-12 河南科技大学 Method for controlling energy-saving series-connection hybrid power tractor

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
H BORHAN,A VAHIDI,AM PHILLIPS,ML KUANG,IV KOLMANOVSKY: "MPC-Based Energy Management of a Power-Split Hybrid Electric Vehicle", 《IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY》 *
H.ALI BORHAN,ARDALAN VAHIDI,ANTHONY M.PHILLIPS,MING L.KUANG,IL: "Predictive Energy Management of a Power-Split Hybrid Electric Vehicle", 《2009 AMERICAN CONTROL CONFERENCE》 *
ZIJIA LU,JIPENG SONG,HONGLIANG YUAN,LING SHEN: "MPC-Based Torque Distribution Strategy for Energy Management of Power-Split Hybrid Electric Vehicles", 《PRECEEDINGS OF THE 32ND CHINESE CONTROL CONFERENCE》 *
曾祥瑞,黄开胜,孟凡博: "具有实时运算潜力的并联混合动力汽车模型预测控制", 《汽车安全与节能学报》 *
赵韩,吴迪: "基于随机模型预测控制的并联式混合动力汽车控制策略研究", 《汽车工程》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107688343A (en) * 2017-08-01 2018-02-13 北京理工大学 A kind of energy control method of motor vehicle driven by mixed power
CN107688343B (en) * 2017-08-01 2019-12-06 北京理工大学 Energy control method of hybrid power vehicle
CN109572665A (en) * 2017-09-29 2019-04-05 通用汽车环球科技运作有限责任公司 Inearized model based on dynamical system MPC
CN111409622A (en) * 2020-01-17 2020-07-14 北京理工大学 Control method and device for electromechanical compound transmission system of tracked vehicle

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Application publication date: 20150923